Segmentation of moving objects in image sequence based on perceptual similarity of local texture and photometric features

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Original languageEnglish
Article number62
Journal / PublicationEurasip Journal on Image and Video Processing
Volume2018
Online published27 Jul 2018
Publication statusPublished - 2018

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Abstract

The segmentation of moving objects in image sequence can be formulated as a background subtraction problem—the separation of objects from the background in each image frame. The background scene is learned and modeled. A pixelwise process is employed to classify each pixel as an object or background based on its similarity with the background model. The segmentation is challenging due to the occurrence of dynamic elements such as illumination change and background motions. We propose a framework for object segmentation with a novel feature for background representation and new mechanisms for updating the background model. A ternary pattern is employed to characterize the local texture. The pattern and photometric features are used for background modeling. The classification of pixel is performed based on the perceptual similarity between the current pixel and the background model. The segmented object is refined by taking into account the spatial consistency of the image feature. For the background model update, we propose two mechanisms that are able to adapt to abrupt background change and also merge new background elements into the model. We compare our framework with various background subtraction algorithms on video datasets.

Research Area(s)

  • Background subtraction, Dynamic background, Local ternary pattern, Moving object segmentation, Video surveillance

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